Layne  Fadel

Layne Fadel

1663974660

Revealing 7 Useful Runtimes/Frontends Libraries for Jupyter

In this Jupyter article, let's learn about Runtimes/Frontends: Revealing 7 Useful Runtimes/Frontends Libraries for Jupyter

Table of contents:

  • JupyterWith - Nix-based framework for the definition of declarative and reproducible Jupyter environments.
  • kaggle/docker-python - Kaggle Python docker image that includes datasets and packages.
  • ML Workspace - Docker image that includes Jupyter(Lab) and various packages for data science/machine learning.
  • nteract - Native desktop notebook frontend.
  • Stencila - Native desktop notebook frontend.
  • Visual Studio Code - Native desktop notebook frontend.
  • voila - Notebooks as interactive standalone web applications.

what is Jupyter?

JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.


Revealing 7 Useful Runtimes/Frontends Libraries for Jupyter

  1. JupyterWith

This repository provides a Nix-based framework for the definition of declarative and reproducible Jupyter environments. These environments include JupyterLab - configurable with extensions - the classic notebook, and configurable Jupyter kernels.

In practice, a Jupyter environment is defined in a single shell.nix file which can be distributed together with a notebook as a self-contained reproducible package.

Getting started

Using Nix-Shell

Nix must be installed in order to use JupyterWith. A simple JupyterLab environment with kernels can be defined in a shell.nix file such as:

let
  jupyter = import (builtins.fetchGit {
    url = https://github.com/tweag/jupyterWith;
    # Example working revision, check out the latest one.
    rev = "45f9a774e981d3a3fb6a1e1269e33b4624f9740e";
  }) {};

  iPython = jupyter.kernels.iPythonWith {
    name = "python";
    packages = p: with p; [ numpy ];
  };

  iHaskell = jupyter.kernels.iHaskellWith {
    name = "haskell";
    packages = p: with p; [ hvega formatting ];
  };

  jupyterEnvironment =
    jupyter.jupyterlabWith {
      kernels = [ iPython iHaskell ];
    };
in
  jupyterEnvironment.env

JupyterLab can then be started by running:

nix-shell --command "jupyter lab"

This can take a while, especially when it is run for the first time because all dependencies of JupyterLab have to be downloaded, built and installed. Subsequent runs are instantaneous for the same environment, or much faster even when some packages or kernels are changed, since a lot will already be cached.

This process can be largely accelerated by using cachix:

cachix use jupyterwith

View on GitHub


2.  docker-python

Kaggle Notebooks allow users to run a Python Notebook in the cloud against our competitions and datasets without having to download data or set up their environment.

This repository includes the Dockerfile for building the CPU-only and GPU image that runs Python Notebooks on Kaggle.

Requesting new packages

First, evaluate whether installing the package yourself in your own notebooks suits your needs. See guide.

If you the first step above doesn't work for your use case, open an issue or a pull request.

Opening a pull request

  1. Edit the Dockerfile.
  2. Follow the instructions below to build a new image.
  3. Add tests for your new package. See this example.
  4. Follow the instructions below to test the new image.
  5. Open a PR on this repo and you are all set!

Building a new image

./build

Flags:

  • --gpu to build an image for GPU.
  • --use-cache for faster iterative builds.

Testing a new image

A suite of tests can be found under the /tests folder. You can run the test using this command:

./test

Flags:

  • --gpu to test the GPU image.

View on GitHub


3.  ml-workspace

All-in-one web-based development environment for machine learning

The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated.

Getting Started

Prerequisites

The workspace requires Docker to be installed on your machine (📖 Installation Guide).

Start single instance

Deploying a single workspace instance is as simple as:

docker run -p 8080:8080 mltooling/ml-workspace:0.13.2

Voilà, that was easy! Now, Docker will pull the latest workspace image to your machine. This may take a few minutes, depending on your internet speed. Once the workspace is started, you can access it via http://localhost:8080.

If started on another machine or with a different port, make sure to use the machine's IP/DNS and/or the exposed port.

To deploy a single instance for productive usage, we recommend to apply at least the following options:

docker run -d \
    -p 8080:8080 \
    --name "ml-workspace" \
    -v "${PWD}:/workspace" \
    --env AUTHENTICATE_VIA_JUPYTER="mytoken" \
    --shm-size 512m \
    --restart always \
    mltooling/ml-workspace:0.13.2

This command runs the container in background (-d), mounts your current working directory into the /workspace folder (-v), secures the workspace via a provided token (--env AUTHENTICATE_VIA_JUPYTER), provides 512MB of shared memory (--shm-size) to prevent unexpected crashes (see known issues section), and keeps the container running even on system restarts (--restart always). You can find additional options for docker run here and workspace configuration options in the section below.

View on GitHub


4.  nteract

nteract is an open-source organization committed to creating fantastic interactive computing experiences that allow people to collaborate with ease.

We build SDKs, applications, and libraries that help you and your team make the most of interactive (particularly Jupyter) notebooks and REPLs.

To learn more about the nteract open source organization and the rest of our projects, please visit our website.

What's in this repo?

This repo is a monorepo. It contains the code for the nteract core SDK and nteract's desktop and web applications. It also contains the documentation for the SDK and the applications. Here's a quick guide to the contents of the monorepo.

FolderDescription
applications/desktopSource code for the nteract desktop application. The desktop application is a cross-platform app built using Electron.
applications/jupyter-extensionSource code the nteract Jupyter extension. This extension can be installed alongside Jupyter classic and JupyterLab in your Jupyter deployments or personal Jupyter server.
packagesJavaScript packages that are part of the nteract core SDK.
changelogsChangelogs for each release of the nteract core SDK and applications.

How do I contribute to this repo?

If you are interested in contributing to nteract, please read the contribution guidelines for information on how to set up your nteract repo for development, how to write tests and validate changes, how to update documentation, and how to submit your code changes for review on GitHub.

View on GitHub


5.  stencila

Stencila is comprised of several open source packages, written in a variety of programming languages. This repo acts as an entry point to these other packages as well as hosting code for our desktop and CLI tools.

We 💕 contributions! All types of contributions: ideas 🤔, examples 💡, bug reports 🐛, documentation 📖, code 💻, questions 💬. If you are unsure of where to make a contribution feel free to open a new issue or discussion in this repository (we can always move them elsewhere if need be).

📜 Help

For documentation, including demos and reference guides, please go to our Help site https://help.stenci.la/. That site is developed in the help folder of this repository and contributions are always welcome.

🎁 Hub

If you don't want to install anything, or just want to try out Stencila, https://hub.stenci.la is the best place to start. It's a web application that makes all our software available via intuitive browser-based interfaces. You can contribute to Stencila Hub at stencila/hub.

🖥️ Desktop

If you'd prefer to use Stencila on your own computer, the Stencila Desktop is a great place to start. It is still in the early stages of (re)development but please see the desktop folder for its current status and how you can help out!

View on GitHub


6.  voila

Rendering of live Jupyter notebooks with interactive widgets.

Introduction

Voilà turns Jupyter notebooks into standalone web applications.

Unlike the usual HTML-converted notebooks, each user connecting to the Voilà tornado application gets a dedicated Jupyter kernel which can execute the callbacks to changes in Jupyter interactive widgets.

  • By default, Voilà disallows execute requests from the front-end, preventing execution of arbitrary code.
  • By default, Voilà runs with the strip_source option, which strips out the input cells from the rendered notebook.

Installation

Voilà can be installed with the mamba (or conda) package manager from conda-forge

mamba install -c conda-forge voila

or from PyPI

pip install voila

JupyterLab preview extension

Voilà provides a JupyterLab extension that displays a Voilà preview of your Notebook in a side-pane.

Starting with JupyterLab 3.0, the extension is automatically installed after installing voila with pip install voila.

If you would like to install the extension from source, run the following command.

jupyter labextension install @voila-dashboards/jupyterlab-preview

View on GitHub


Related videos:

From Jupyter Notebook to Production Web App, with Anvil and (only) Python


Related posts:

#jupyter 

What is GEEK

Buddha Community

Revealing 7 Useful Runtimes/Frontends Libraries for Jupyter
Chloe  Butler

Chloe Butler

1667425440

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

  1. Design the PCB using your favorite CAD or drawing software.
  2. Print the top and bottom copper and top silk screen layers to a PDF file.
  3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
  4. Use a Gerber viewer to double-check the output against the original PCB design.
  5. Make adjustments as needed.
  6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.


pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl 

Hire Frontend Developers

Create a new web app or revamp your existing website?

Every existing website or a web application that we see with an interactive and user-friendly interface are from Front-End developers who ensure that all visual effects come into existence. Hence, to build a visually appealing web app front-end development is required.

At HourlyDeveloper.io, you can Hire FrontEnd Developers as we have been actively working on new frontend development as well as frontend re-engineering projects from older technologies to newer.

Consult with experts: https://bit.ly/2YLhmFZ

#hire frontend developers #frontend developers #frontend development company #frontend development services #frontend development #frontend

How to Send E-mail Using Queue in Laravel 7/8

Today I will show you How to Send E-mail Using Queue in Laravel 7/8, many time we can see some process take more time to load like payment gateway, email send, etc. Whenever you are sending email for verification then it load time to send mail because it is services. If you don’t want to wait to user for send email or other process on loading server side process then you can use queue.

Read More : How to Send E-mail Using Queue in Laravel 7/8

https://websolutionstuff.com/post/how-to-send-e-mail-using-queue-in-laravel-7-8


Read Also : Send Mail Example In Laravel 8

https://websolutionstuff.com/post/send-mail-example-in-laravel-8

#how to send e-mail using queue in laravel 7/8 #email #laravel #send mail using queue in laravel 7 #laravel 7/8 send mail using queue #laravel 7/8 mail queue example

Layne  Fadel

Layne Fadel

1663974660

Revealing 7 Useful Runtimes/Frontends Libraries for Jupyter

In this Jupyter article, let's learn about Runtimes/Frontends: Revealing 7 Useful Runtimes/Frontends Libraries for Jupyter

Table of contents:

  • JupyterWith - Nix-based framework for the definition of declarative and reproducible Jupyter environments.
  • kaggle/docker-python - Kaggle Python docker image that includes datasets and packages.
  • ML Workspace - Docker image that includes Jupyter(Lab) and various packages for data science/machine learning.
  • nteract - Native desktop notebook frontend.
  • Stencila - Native desktop notebook frontend.
  • Visual Studio Code - Native desktop notebook frontend.
  • voila - Notebooks as interactive standalone web applications.

what is Jupyter?

JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality.


Revealing 7 Useful Runtimes/Frontends Libraries for Jupyter

  1. JupyterWith

This repository provides a Nix-based framework for the definition of declarative and reproducible Jupyter environments. These environments include JupyterLab - configurable with extensions - the classic notebook, and configurable Jupyter kernels.

In practice, a Jupyter environment is defined in a single shell.nix file which can be distributed together with a notebook as a self-contained reproducible package.

Getting started

Using Nix-Shell

Nix must be installed in order to use JupyterWith. A simple JupyterLab environment with kernels can be defined in a shell.nix file such as:

let
  jupyter = import (builtins.fetchGit {
    url = https://github.com/tweag/jupyterWith;
    # Example working revision, check out the latest one.
    rev = "45f9a774e981d3a3fb6a1e1269e33b4624f9740e";
  }) {};

  iPython = jupyter.kernels.iPythonWith {
    name = "python";
    packages = p: with p; [ numpy ];
  };

  iHaskell = jupyter.kernels.iHaskellWith {
    name = "haskell";
    packages = p: with p; [ hvega formatting ];
  };

  jupyterEnvironment =
    jupyter.jupyterlabWith {
      kernels = [ iPython iHaskell ];
    };
in
  jupyterEnvironment.env

JupyterLab can then be started by running:

nix-shell --command "jupyter lab"

This can take a while, especially when it is run for the first time because all dependencies of JupyterLab have to be downloaded, built and installed. Subsequent runs are instantaneous for the same environment, or much faster even when some packages or kernels are changed, since a lot will already be cached.

This process can be largely accelerated by using cachix:

cachix use jupyterwith

View on GitHub


2.  docker-python

Kaggle Notebooks allow users to run a Python Notebook in the cloud against our competitions and datasets without having to download data or set up their environment.

This repository includes the Dockerfile for building the CPU-only and GPU image that runs Python Notebooks on Kaggle.

Requesting new packages

First, evaluate whether installing the package yourself in your own notebooks suits your needs. See guide.

If you the first step above doesn't work for your use case, open an issue or a pull request.

Opening a pull request

  1. Edit the Dockerfile.
  2. Follow the instructions below to build a new image.
  3. Add tests for your new package. See this example.
  4. Follow the instructions below to test the new image.
  5. Open a PR on this repo and you are all set!

Building a new image

./build

Flags:

  • --gpu to build an image for GPU.
  • --use-cache for faster iterative builds.

Testing a new image

A suite of tests can be found under the /tests folder. You can run the test using this command:

./test

Flags:

  • --gpu to test the GPU image.

View on GitHub


3.  ml-workspace

All-in-one web-based development environment for machine learning

The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. This workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch, Keras, Sklearn) and dev tools (e.g., Jupyter, VS Code, Tensorboard) perfectly configured, optimized, and integrated.

Getting Started

Prerequisites

The workspace requires Docker to be installed on your machine (📖 Installation Guide).

Start single instance

Deploying a single workspace instance is as simple as:

docker run -p 8080:8080 mltooling/ml-workspace:0.13.2

Voilà, that was easy! Now, Docker will pull the latest workspace image to your machine. This may take a few minutes, depending on your internet speed. Once the workspace is started, you can access it via http://localhost:8080.

If started on another machine or with a different port, make sure to use the machine's IP/DNS and/or the exposed port.

To deploy a single instance for productive usage, we recommend to apply at least the following options:

docker run -d \
    -p 8080:8080 \
    --name "ml-workspace" \
    -v "${PWD}:/workspace" \
    --env AUTHENTICATE_VIA_JUPYTER="mytoken" \
    --shm-size 512m \
    --restart always \
    mltooling/ml-workspace:0.13.2

This command runs the container in background (-d), mounts your current working directory into the /workspace folder (-v), secures the workspace via a provided token (--env AUTHENTICATE_VIA_JUPYTER), provides 512MB of shared memory (--shm-size) to prevent unexpected crashes (see known issues section), and keeps the container running even on system restarts (--restart always). You can find additional options for docker run here and workspace configuration options in the section below.

View on GitHub


4.  nteract

nteract is an open-source organization committed to creating fantastic interactive computing experiences that allow people to collaborate with ease.

We build SDKs, applications, and libraries that help you and your team make the most of interactive (particularly Jupyter) notebooks and REPLs.

To learn more about the nteract open source organization and the rest of our projects, please visit our website.

What's in this repo?

This repo is a monorepo. It contains the code for the nteract core SDK and nteract's desktop and web applications. It also contains the documentation for the SDK and the applications. Here's a quick guide to the contents of the monorepo.

FolderDescription
applications/desktopSource code for the nteract desktop application. The desktop application is a cross-platform app built using Electron.
applications/jupyter-extensionSource code the nteract Jupyter extension. This extension can be installed alongside Jupyter classic and JupyterLab in your Jupyter deployments or personal Jupyter server.
packagesJavaScript packages that are part of the nteract core SDK.
changelogsChangelogs for each release of the nteract core SDK and applications.

How do I contribute to this repo?

If you are interested in contributing to nteract, please read the contribution guidelines for information on how to set up your nteract repo for development, how to write tests and validate changes, how to update documentation, and how to submit your code changes for review on GitHub.

View on GitHub


5.  stencila

Stencila is comprised of several open source packages, written in a variety of programming languages. This repo acts as an entry point to these other packages as well as hosting code for our desktop and CLI tools.

We 💕 contributions! All types of contributions: ideas 🤔, examples 💡, bug reports 🐛, documentation 📖, code 💻, questions 💬. If you are unsure of where to make a contribution feel free to open a new issue or discussion in this repository (we can always move them elsewhere if need be).

📜 Help

For documentation, including demos and reference guides, please go to our Help site https://help.stenci.la/. That site is developed in the help folder of this repository and contributions are always welcome.

🎁 Hub

If you don't want to install anything, or just want to try out Stencila, https://hub.stenci.la is the best place to start. It's a web application that makes all our software available via intuitive browser-based interfaces. You can contribute to Stencila Hub at stencila/hub.

🖥️ Desktop

If you'd prefer to use Stencila on your own computer, the Stencila Desktop is a great place to start. It is still in the early stages of (re)development but please see the desktop folder for its current status and how you can help out!

View on GitHub


6.  voila

Rendering of live Jupyter notebooks with interactive widgets.

Introduction

Voilà turns Jupyter notebooks into standalone web applications.

Unlike the usual HTML-converted notebooks, each user connecting to the Voilà tornado application gets a dedicated Jupyter kernel which can execute the callbacks to changes in Jupyter interactive widgets.

  • By default, Voilà disallows execute requests from the front-end, preventing execution of arbitrary code.
  • By default, Voilà runs with the strip_source option, which strips out the input cells from the rendered notebook.

Installation

Voilà can be installed with the mamba (or conda) package manager from conda-forge

mamba install -c conda-forge voila

or from PyPI

pip install voila

JupyterLab preview extension

Voilà provides a JupyterLab extension that displays a Voilà preview of your Notebook in a side-pane.

Starting with JupyterLab 3.0, the extension is automatically installed after installing voila with pip install voila.

If you would like to install the extension from source, run the following command.

jupyter labextension install @voila-dashboards/jupyterlab-preview

View on GitHub


Related videos:

From Jupyter Notebook to Production Web App, with Anvil and (only) Python


Related posts:

#jupyter 

Why Use WordPress? What Can You Do With WordPress?

Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?

WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:

1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.

2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.

3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.

4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.

5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.

6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.

Read More

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